Benchmark Patterns - Methodology Comparaison

Méthodologie benchmarks robustes pour décisions tech.

Concept

Comparer solutions avec données mesurables, pas opinions.

Dimensions Benchmark

1. Performance

Metrics :

• Throughput (req/s, queries/s)
• Latency (p50, p95, p99 ms)
• Memory footprint (MB RAM)
• CPU usage (% cores)
• Scalability (linear/sublinear)

Tools :

• HTTP: wrk, k6, Apache Bench
• DB: sysbench, pgbench
• Load: Locust, Gatling

Methodology :

# Example FastAPI benchmark
wrk -t12 -c400 -d30s http://localhost:8000/api/endpoint
# Output: Requests/sec, Latency distribution

Reporting :

{
  "framework": "FastAPI",
  "requests_per_sec": 25000,
  "latency_p50": 15,
  "latency_p95": 45,
  "latency_p99": 120,
  "memory_mb": 180
}

2. Developer Experience (DX)

Metrics :

• Time to "Hello World" (minutes)
• Documentation completeness (1-10 score)
• Error messages clarity (examples count)
• Type safety (static typing %)
• IDE support (autocomplete quality)

Methodology :

1. Fresh project setup (timer start)
2. Implement simple feature
3. Debug intentional error
4. Rate experience (1-10 scale)

Reporting :

{
  "framework": "FastAPI",
  "hello_world_minutes": 5,
  "docs_score": 9,
  "type_safety": "full",
  "error_messages": "excellent",
  "dx_score": 8.5
}

3. Ecosystem

Metrics :

• GitHub stars (popularity)
• Contributors count (community)
• Recent commits (maintenance)
• Open issues response time (support)
• Plugins/extensions count
• NPM/PyPI downloads trend (6 months)
• Stack Overflow questions (adoption)
• Security advisories (CVE count)

Sources :

• GitHub API
• NPM trends / PyPI stats
• Stack Overflow Trends
• Snyk / CVE databases

Reporting :

{
  "library": "FastAPI",
  "github_stars": 75000,
  "contributors": 500,
  "recent_commits_30d": 150,
  "pypi_downloads_monthly": 5000000,
  "stackoverflow_questions": 12000,
  "cve_count": 2,
  "ecosystem_score": 9.0
}

4. Cost

Metrics :

• Infrastructure cost ($/month 10k users)
• License fees
• Developer hours (implementation time)
• Training cost (team onboarding)

Methodology :

Cost = Infra + License + (Dev_hours × hourly_rate) + Training

Reporting :

{
  "solution": "FastAPI + PostgreSQL",
  "infra_monthly_usd": 50,
  "license_fees": 0,
  "implementation_hours": 80,
  "training_hours": 16,
  "total_first_year_usd": 14400
}

5. Team Fit

Metrics :

• Current expertise (team members count)
• Learning curve (weeks to proficiency)
• Hiring pool (candidates available)
• Onboarding time (new dev productive)

Methodology :

• Survey team: "Rate expertise 1-10"
• Estimate learning time (expert opinion)
• Check job boards (candidates count)

Reporting :

{
  "technology": "Python/FastAPI",
  "team_expertise_avg": 7.5,
  "learning_curve_weeks": 2,
  "hiring_pool": "large",
  "team_fit_score": 8.0
}

Benchmark Workflow

1. Define criteria (performance, DX, cost, etc.)
2. Weight criteria (totals 100%)
3. Create POC minimal (each solution)
4. Run benchmarks (standardized)
5. Collect metrics
6. Score each solution (1-10)
7. Calculate weighted total
8. Document in ADR

Evaluation Matrix Template

Calculation :

Sol1 = (9×0.30) + (8×0.25) + (7×0.20) + (6×0.15) + (8×0.10)
     = 2.70 + 2.00 + 1.40 + 0.90 + 0.80
     = 8.0

Output Format

{
  "benchmarks": [
    {
      "solution": "FastAPI",
      "performance": {"req_s": 25000, "latency_p95": 45},
      "dx": {"score": 8.5},
      "ecosystem": {"score": 9.0},
      "cost": {"monthly_usd": 50},
      "team_fit": {"score": 8.0},
      "weighted_score": 8.0
    },
    {
      "solution": "Django REST",
      "performance": {"req_s": 5000, "latency_p95": 120},
      "dx": {"score": 7.0},
      "ecosystem": {"score": 9.5},
      "cost": {"monthly_usd": 60},
      "team_fit": {"score": 9.0},
      "weighted_score": 7.1
    }
  ],
  "winner": "FastAPI",
  "margin": 0.9
}

Integration with ADR

ADR references benchmarks :

• Evaluation matrix → Scoring section
• Benchmarks JSON → Options détaillées
• Winner determination → Decision rationale

Workflow :

1. Run benchmarks → benchmarks.json
2. Create ADR → Reference benchmarks.json
3. Score options → Evaluation matrix
4. Document rationale → Links to benchmark sources